Hacker News Reader

I wonder how FastText (essentially word2vec + word & char n-grams + other stuff) stacks up against these algorithms.

In my own tests on my own corpuses, CPU-based FastText is faster to train and produces significantly better results (precision/recall) than the GPU-bound CNN algorithms that I've tried, but have not compared it against RNN techniques.

View on HN

I’ve found that some CNNs consistently beat fasttext in terms of model quality. But I’ve beaten those CNNs and fasttext by doing transfer learning with ULMFit and fasts I. But if we’re talking training speed, fasttext is indeed aptly named.

FastText is essentially a linear classifier and it's not surprising that it would train quickly. As for the prediction metrics, I imagine that will depend on the type of data and problem you're working with. Linear models perform very well on certain types of problems (I've had great success with SVMs on text classification problems personally) but for more complicated tasks, I imagine that the deep learning models would perform very, very well (relatively).

Reply on news.ycombinator.com